We consider learning the stochastic generalised linear bandit under delayed feedback. As previously mentioned, existing approaches for this setting require constant or light-tailed delays to derive performance guarantees [1]}, [2]}. These assumptions are a limiting factor in their theory, as empirical evidence suggests that the delays have a heavy-tailed nature [3]}.
